{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ddad30e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Entering Chat REPL =====\n",
      "Type \"exit\" to exit.\n",
      "\n",
      "Assistant: 你好！很高兴见到你。有什么我可以帮忙的吗？\n",
      "\n",
      "Assistant: 你好！很高兴再次与你交流。如果你有任何问题或需要帮助，请随时告诉我。无论是学习、工作还是生活中的疑问，我都会尽力提供帮助。\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 25\u001b[0m\n\u001b[1;32m     22\u001b[0m Settings\u001b[38;5;241m.\u001b[39mllm \u001b[38;5;241m=\u001b[39m deepseek_llm()\n\u001b[1;32m     24\u001b[0m chat_engine\u001b[38;5;241m=\u001b[39mSimpleChatEngine\u001b[38;5;241m.\u001b[39mfrom_defaults()\n\u001b[0;32m---> 25\u001b[0m \u001b[43mchat_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstreaming_chat_repl\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/danwen/lib/python3.11/site-packages/llama_index/core/chat_engine/types.py:388\u001b[0m, in \u001b[0;36mBaseChatEngine.streaming_chat_repl\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    386\u001b[0m response\u001b[38;5;241m.\u001b[39mprint_response_stream()\n\u001b[1;32m    387\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 388\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHuman: \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/danwen/lib/python3.11/site-packages/ipykernel/kernelbase.py:1282\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1280\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1281\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[0;32m-> 1282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1283\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1284\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent_ident\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1285\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_parent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1286\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1287\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/danwen/lib/python3.11/site-packages/ipykernel/kernelbase.py:1325\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1322\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1323\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[1;32m   1324\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1325\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1326\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1327\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    }
   ],
   "source": [
    "from llama_index.llms.openai import OpenAI \n",
    "from llama_index.core.chat_engine import SimpleChatEngine\n",
    "from typing import Dict\n",
    "\n",
    "from llama_index.llms.openai import OpenAI as DeepSeeK\n",
    "from openai import OpenAI\n",
    "from llama_index.llms.openai.utils import ALL_AVAILABLE_MODELS, CHAT_MODELS\n",
    "DEEPSEEK_MODELS: Dict[str, int] = {\n",
    "    \"deepseek-chat\": 128000,\n",
    "}\n",
    "ALL_AVAILABLE_MODELS.update(DEEPSEEK_MODELS)\n",
    "CHAT_MODELS.update(DEEPSEEK_MODELS)\n",
    "\n",
    "def deepseek_llm(**kwargs):\n",
    "    llm = DeepSeeK(api_key='sk-79fa0380ce9c4c3297e51451baab0d09',\n",
    "                 model=\"deepseek-chat\",\n",
    "                 api_base=\"https://api.deepseek.com/v1\",\n",
    "                 temperature=0.7,\n",
    "                 **kwargs)\n",
    "    return llm\n",
    "from llama_index.core import Settings\n",
    "Settings.llm = deepseek_llm()\n",
    "\n",
    "chat_engine=SimpleChatEngine.from_defaults()\n",
    "chat_engine.streaming_chat_repl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28c98197",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91096c5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Entering Chat REPL =====\n",
      "Type \"exit\" to exit.\n",
      "\n",
      "Assistant: 你好！有什么我可以帮助你的吗？\n",
      "\n",
      "Assistant: 如果你有任何问题或需要帮助，请随时告诉我！\n",
      "\n",
      "Assistant: 如果你有任何事情想聊，或者有什么问题需要解决，别犹豫，直接告诉我就好！\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from llama_index.llms.openai import OpenAI \n",
    "from llama_index.core.chat_engine import SimpleChatEngine\n",
    "from typing import Dict\n",
    "\n",
    "from llama_index.llms.openai import OpenAI as DeepSeeK\n",
    "from openai import OpenAI\n",
    "from llama_index.llms.openai.utils import ALL_AVAILABLE_MODELS, CHAT_MODELS\n",
    "# DEEPSEEK_MODELS: Dict[str, int] = {\n",
    "#     \"deepseek-chat\": 128000,\n",
    "# }\n",
    "# ALL_AVAILABLE_MODELS.update(DEEPSEEK_MODELS)\n",
    "# CHAT_MODELS.update(DEEPSEEK_MODELS)\n",
    "\n",
    "def deepseek_llm(**kwargs):\n",
    "    llm = DeepSeeK(api_key='sk-lYp1RcVMaX4uHjcnLasZ75DPNrpXSxx0dJTDxBCKZ1TJKHPi',\n",
    "                 model=\"gpt-4o\",\n",
    "                 api_base=\"https://xiaoai.plus/v1\",\n",
    "                 temperature=0.7,\n",
    "                 **kwargs)\n",
    "    return llm\n",
    "from llama_index.core import Settings\n",
    "Settings.llm = deepseek_llm()\n",
    "\n",
    "chat_engine=SimpleChatEngine.from_defaults()\n",
    "chat_engine.streaming_chat_repl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57ab4054",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18990619",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "172bb8af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18774650",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f0dd5ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e80dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import Settings\n",
    "from .embeddings import embed_model_local_bge_small\n",
    "from .llms import deepseek_llm\n",
    "Settings.embed_model = embed_model_local_bge_small()\n",
    "Settings.llm = deepseek_llm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc86a716",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b578767",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7abed06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "14baaf34f96f8d37",
   "metadata": {
    "id": "14baaf34f96f8d37"
   },
   "source": [
    "# 3、导入依赖包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2778883a4041299",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T09:14:29.281842Z",
     "start_time": "2024-12-13T09:14:29.268301Z"
    },
    "id": "e2778883a4041299"
   },
   "outputs": [],
   "source": [
    "from llama_index.core import SimpleDirectoryReader,VectorStoreIndex,Settings,get_response_synthesizer,StorageContext,load_index_from_storage,Document\n",
    "from typing import Dict\n",
    "from llama_index.llms.openai import OpenAI as DeepSeeK\n",
    "from llama_index.llms.openai.utils import ALL_AVAILABLE_MODELS, CHAT_MODELS\n",
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "from llama_index.core.query_engine import RetrieverQueryEngine\n",
    "from llama_index.core.retrievers import VectorIndexRetriever"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94961e3096275c74",
   "metadata": {
    "id": "94961e3096275c74"
   },
   "source": [
    "# 4、设置LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53f0ccc8f2e37f80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T05:46:51.430867Z",
     "start_time": "2024-12-13T05:46:51.422016Z"
    },
    "id": "53f0ccc8f2e37f80"
   },
   "outputs": [],
   "source": [
    "# \n",
    "DEEPSEEK_MODELS: Dict[str, int] = {\n",
    "    \"deepseek-chat\": 128000,\n",
    "}\n",
    "ALL_AVAILABLE_MODELS.update(DEEPSEEK_MODELS)\n",
    "CHAT_MODELS.update(DEEPSEEK_MODELS)\n",
    "\n",
    "llm = DeepSeeK(api_key='sk-79fa0380ce9c4c3297e51451baab0d09',\n",
    "                 model=\"deepseek-chat\",\n",
    "                 api_base=\"https://api.deepseek.com/v1\",\n",
    "                 temperature=0.7)\n",
    "\n",
    "\n",
    "Settings.llm = llm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182f2fdfa0e8414",
   "metadata": {
    "id": "182f2fdfa0e8414"
   },
   "source": [
    "# 5、设置嵌入模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac4e106644c4ed34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T05:47:45.733162Z",
     "start_time": "2024-12-13T05:47:45.153899Z"
    },
    "id": "ac4e106644c4ed34"
   },
   "outputs": [],
   "source": [
    "\n",
    "embed_model = HuggingFaceEmbedding(model_name=\"/home/tom/llms/bge-small-zh-v1.5\")\n",
    "Settings.embed_model = embed_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c642b63ec07d7053",
   "metadata": {
    "id": "c642b63ec07d7053"
   },
   "source": [
    "## 加载单个pdf文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b1ab34a5a0b80f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T02:42:46.306569Z",
     "start_time": "2024-12-13T02:42:45.110618Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7b1ab34a5a0b80f5",
    "outputId": "033c44c6-fc1a-43c0-a149-d9462b1ec8b0"
   },
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(input_files=[\"/home/tom/my_learn/my_danwen/0_playgrounds/0_test_data/SQL.pdf\"]).load_data()\n",
    "print(len(documents))\n",
    "print(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428fd6bfa6e83e85",
   "metadata": {
    "id": "428fd6bfa6e83e85"
   },
   "source": [
    "## 加载单个文本文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb3362ffe4f7bf4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T03:45:36.161313Z",
     "start_time": "2024-12-13T03:45:36.050174Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cb3362ffe4f7bf4a",
    "outputId": "56cae55b-3ed3-4f89-86b0-495de5db7229"
   },
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(input_files=[\"/home/tom/my_learn/my_danwen/0_playgrounds/0_test_data/hangzhou.txt\"]).load_data()\n",
    "print(len(documents))\n",
    "print(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f693700ba75e4ae0",
   "metadata": {
    "id": "f693700ba75e4ae0"
   },
   "source": [
    "## 加载多个文本文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5b56f5b708e4a11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T05:51:26.461506Z",
     "start_time": "2024-12-13T05:51:26.402207Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e5b56f5b708e4a11",
    "outputId": "ead0d9f0-173d-4ae6-84dd-f03229d5eddf"
   },
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(input_dir=\"data\", required_exts=[\".txt\"]).load_data()\n",
    "print(len(documents))\n",
    "print(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "_s259zg3zUd2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_s259zg3zUd2",
    "outputId": "6c9d7290-100d-4517-cd12-84b55e262e38"
   },
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(input_dir=\"data\").load_data()\n",
    "print(len(documents))\n",
    "print(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67be8fdf9c4ae750",
   "metadata": {
    "id": "67be8fdf9c4ae750"
   },
   "source": [
    "# 7、LLmaindex中的Node是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67d4114caf71f34",
   "metadata": {
    "id": "a67d4114caf71f34"
   },
   "source": [
    "LlamaIndex 中的 Node 对象表示源文档的“块”或部分。\n",
    "可能是一个文本块、一幅图像或其他类型的数据。类似于 Documents，Nodes 也包含与其他节点的元数据和关系信息。\n",
    "\n",
    "在 LlamaIndex 中，Nodes 被认为是一等公民。\n",
    "这意味着可以直接定义 Nodes 及其所有属性。或者也可以使用 NodeParser 类将源Document解析为Node。默认情况下，从文档派生的每个节点都会继承相同的元数据。例如，文档中的file_name字段会传播到每个节点。\n",
    "\n",
    "视特定的使用情况和数据结构，选择将整个 Document 对象发送到索引还是在索引之前将 Document 转换为 Node 对象取决于自己。\n",
    "\n",
    "1、将整个Document对象发送至索引：这种方法适用于将整个文档作为单个单位进行维护。当您的文档相对较短或不同部分之间的上下文重要时这可能会更好。（与文档直接对话）\n",
    "\n",
    "2、在索引之前将Document转换为Node对象：当你的文档很长且希望在索引之前将其拆分成较小块（或节点）时，这种方法很实用。当想要检索文档特定部分而非整个文档时这可能会更好。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0b2c20d3ed3d76",
   "metadata": {
    "id": "ab0b2c20d3ed3d76"
   },
   "source": [
    "## 索引文本文档（将整个Document对象发送至索引）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb388204a239acbc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T06:13:06.179725Z",
     "start_time": "2024-12-13T06:02:58.905243Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153,
     "referenced_widgets": [
      "726acbcef88d4ad196e2ae47d6502536",
      "1595b3cb1dab4fabbbff083d01d01409",
      "3f3d3d377a2445008849fc272811b03d",
      "8ee6a569630642528c97ce861ee53a51",
      "77d8432be208424c92c0cf2c67cee038",
      "e3796a2052674f35ba882fc58282616e",
      "334f83306ab04341b72095a6c5958a77",
      "3619a4bcab1a41c18b00b603bbbd6dbc",
      "385f074d7baf422f8d0ca4614bc88687",
      "3d8f6006ab124b1a85f533c6f186ce2a",
      "2d6e623b90fe4536b1f6dd03c35a7bf7",
      "f3d1e17c03ed491d8e8aa01d1c132e76",
      "a4a2391a7a4a48fe92d4e352ab28ba44",
      "9aab343e76524fd0a56a1da7ffc6050a",
      "60bfc0495834430ba501e170b90ab54c",
      "715a9716c6a44628ab6f9dbddaabba8e",
      "f0a9a995f98d43339bfca7f226952046",
      "44d5d650eb464d21923005ac4bb3597e",
      "9127d360a94a44be82371d1e28196d07",
      "620d23e5af8f4c2f867b8e03d1b741ee",
      "5ae8872a79e44c3eb95b163ea62600ff",
      "76b8285c942a49b8970f6e9120d6201b"
     ]
    },
    "id": "cb388204a239acbc",
    "outputId": "47fc0ddd-c25f-4bbd-8195-d74c3f53d535"
   },
   "outputs": [],
   "source": [
    "# documents = SimpleDirectoryReader(input_files=[\"/home/tom/my_learn/my_danwen/0_playgrounds/0_test_data/hangzhou.txt\"]).load_data()\n",
    "# index = VectorStoreIndex.from_documents(documents, show_progress=True)\n",
    "# print(len(documents))\n",
    "# print(documents)\n",
    "# print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pz9TauA_liXc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104,
     "referenced_widgets": [
      "fa0a30a5c21943a9b20229f9ca908154",
      "f5dcff81d1464099bf9725e285327b55",
      "a3795c6a3791458ebe7f105dd003ebf0",
      "3102bc2eb3cd43c197b87e81887ee950",
      "120479b38da6432796f3584dc7665bbd",
      "4d5b2fde517b42429b22abc9821ba008",
      "410c76356b024a53a2445f64daf8e5b9",
      "5f1f8aa1489c432898396474cfed2786",
      "7380aac39ac04056af162dab6119575a",
      "c76a7a3a6e794150998bda8f6cf69363",
      "7a3ce2cd03644ad1928dbda21784d3d5"
     ]
    },
    "id": "pz9TauA_liXc",
    "outputId": "7f231d83-bfbc-4e04-de39-209ef96773f1"
   },
   "outputs": [],
   "source": [
    "# 先创建node再创建索引\n",
    "documents = SimpleDirectoryReader(input_files=[\"/home/tom/my_learn/my_danwen/0_playgrounds/0_test_data/SQL.pdf\"]).load_data()\n",
    "# 创建一个句子分割器\n",
    "node_parser = SentenceSplitter.from_defaults()\n",
    "# 从文档中获取节点\n",
    "nodes = node_parser.get_nodes_from_documents(documents)\n",
    "print(len(nodes))\n",
    "print(nodes)\n",
    "index = VectorStoreIndex(nodes, show_progress=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hhshu7lY20iE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hhshu7lY20iE",
    "outputId": "d470117e-42c0-4d01-d84d-dc64c3a43af2"
   },
   "outputs": [],
   "source": [
    "# 默认召回的节点数 top_k=2\n",
    "# 如果top_k=1，获取不到想要的答案\n",
    "retriever = index.as_retriever(similarity_top_k=5)\n",
    "response = retriever.retrieve(\"第50题sql的答案是什么？\")\n",
    "print(response)\n",
    "print(len(response))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7cDZZGw9807",
   "metadata": {
    "id": "d7cDZZGw9807"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "43314fc6033ee89d",
   "metadata": {
    "id": "43314fc6033ee89d"
   },
   "source": [
    "## 索引文本文档（在索引之前将Document转换为Node对象）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2162ccdf2757036",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T06:26:03.352614Z",
     "start_time": "2024-12-13T06:16:18.051176Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2162ccdf2757036",
    "outputId": "355bd934-cf33-4594-fe06-4ec46fa9e40d"
   },
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(input_files=[\"/home/tom/my_learn/my_danwen/0_playgrounds/0_test_data/围城.txt\"]).load_data()\n",
    "# 创建一个句子分割器\n",
    "node_parser = SentenceSplitter.from_defaults(chunk_size=1024)\n",
    "# 从文档中获取节点\n",
    "nodes = node_parser.get_nodes_from_documents(documents)\n",
    "print(len(nodes))\n",
    "print(nodes)\n",
    "# 创建向量存储索引\n",
    "# index = VectorStoreIndex(nodes)\n",
    "# print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62Lpofb0CSiN",
   "metadata": {
    "id": "62Lpofb0CSiN"
   },
   "outputs": [],
   "source": [
    "# 默认召回的节点数 top_k=2\n",
    "# 如果top_k=1，获取不到想要的答案\n",
    "index = VectorStoreIndex(nodes, show_progress=True)\n",
    "retriever = index.as_retriever(similarity_top_k=6)\n",
    "response = retriever.retrieve(\"方鸿渐的妻子是谁？\")\n",
    "print(response)\n",
    "print(len(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5707b8ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_engine = index.as_query_engine(similarity_top_k=6)\n",
    "response = query_engine.query(\"方鸿渐的妻子是谁？\")\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bjZkgtziwSE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4bjZkgtziwSE",
    "outputId": "b69537c3-4009-4e8a-810c-37d50581d0da"
   },
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(input_files=[\"data/SQL.pdf\"]).load_data()\n",
    "# 创建一个句子分割器\n",
    "node_parser = SentenceSplitter.from_defaults(chunk_size=1024)\n",
    "# 从文档中获取节点\n",
    "nodes = node_parser.get_nodes_from_documents(documents)\n",
    "print(len(nodes))\n",
    "print(nodes)\n",
    "# 创建向量存储索引\n",
    "# index = VectorStoreIndex(nodes)\n",
    "# print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "IxOHnxLJjkAP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IxOHnxLJjkAP",
    "outputId": "9f2c3cbc-fccf-4bdd-e3b4-f7bbd860e0f3"
   },
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(input_files=[\"data/SQL.pdf\"]).load_data()\n",
    "# doc = Document(text=\"\\n\\n\".join([d.text for d in documents[0:]]))\n",
    "# 创建一个句子分割器\n",
    "node_parser = SentenceSplitter.from_defaults(chunk_size=1024)\n",
    "# 从文档中获取节点\n",
    "nodes = node_parser.get_nodes_from_documents(documents)\n",
    "print(len(nodes))\n",
    "print(nodes)\n",
    "# 创建向量存储索引\n",
    "index = VectorStoreIndex(nodes)\n",
    "# print(index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770c84591d67fc2d",
   "metadata": {
    "id": "770c84591d67fc2d"
   },
   "source": [
    "# 8、LlamaIndex 中的 VectorStoreIndex 是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4aabb9a73e7c77",
   "metadata": {
    "id": "3e4aabb9a73e7c77"
   },
   "source": [
    "VectorStoreIndex 是一种索引类型，它使用文本的向量表示以实现有效检索相关上下文。\n",
    "\n",
    "它构建在 VectorStore 之上，后者是一种存储向量并允许快速最近邻搜索的数据结构。\n",
    "VectorStoreIndex 接收 IndexNode 对象，这些对象代表了原始文档的块。\n",
    "它使用一个嵌入模型（在ServiceContext中指定）将这些节点的文本内容转换成向量表示。然后这些向量被存储在VectorStore中。\n",
    "\n",
    "在查询时，VectorStoreIndex 可以快速检索出针对特定查询最相关的节点。 它通过使用相同的嵌入模型将查询转换为向量，然后在 VectorStore 中执行最近邻搜索来实现这一点。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "317a2cf32439acd0",
   "metadata": {
    "id": "317a2cf32439acd0"
   },
   "source": [
    "# 9、LlamaIndex 中的 retriever 是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ef53ba7c086953",
   "metadata": {
    "id": "27ef53ba7c086953"
   },
   "source": [
    "在LlamaIndex中，\"retriever\"是指从索引中检索相关上下文的组件。具体来说，它是一个负责从索引中获取与用户查询相关的IndexNode对象的系统。这些IndexNode对象代表了存储在索引中的原始文档块，它们可以是文本块、图像或其他类型的数据。\n",
    "\n",
    "在LlamaIndex的架构中，retriever通常与一个查询引擎（如RetrieverQueryEngine）一起工作。RetrieverQueryEngine使用retriever来从VectorStoreIndex（一种基于向量的索引类型）中检索相关的IndexNode对象。这个过程涉及到将用户的查询转换为向量表示，然后在VectorStore中执行最近邻搜索，以找到与查询最相关的节点。\n",
    "\n",
    "retriever在LlamaIndex中的作用可以总结如下：\n",
    "\n",
    "1. **检索相关节点**：根据用户的查询，retriever从索引中检索出最相关的IndexNode对象。\n",
    "2. **支持RAG用例**：对于“检索增强生成”（RAG）的应用场景，retriever是关键组件，它允许系统快速检索与查询相关的上下文信息。\n",
    "3. **提高响应质量**：通过提供相关的上下文，retriever帮助生成更准确和信息丰富的响应。\n",
    "\n",
    "在实际应用中，retriever的性能和准确性对于整个系统的效能至关重要，因为它直接影响到检索结果的相关性和最终生成的响应的质量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b613d90b08b7bffa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T09:06:25.313038Z",
     "start_time": "2024-12-13T09:06:24.532268Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b613d90b08b7bffa",
    "outputId": "e21ae955-e6d4-4a74-a25d-6f5bd658d112"
   },
   "outputs": [],
   "source": [
    "retriever = index.as_retriever(similarity_top_k=5)\n",
    "nodes = retriever.retrieve(\"介绍一下杭州的历史\")\n",
    "print(len(nodes))\n",
    "print(nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ffbb00875a62c7",
   "metadata": {
    "id": "94ffbb00875a62c7"
   },
   "source": [
    "# 10、LlamaIndex 中的 RetrieverQueryEngine 是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e935d4a3ef25fb6",
   "metadata": {
    "id": "2e935d4a3ef25fb6"
   },
   "source": [
    "LlamaIndex 中的 RetrieverQueryEngine 是一种查询引擎，它使用一个检索器从索引中获取相关的上下文，给定用户查询。\n",
    "它主要用于和检索器一起工作，比如从 VectorStoreIndex 创建的 VectorStoreRetriever。\n",
    "RetrieverQueryEngine 接受一个检索器和一个响应合成器作为输入。 检索器负责从索引中获取相关的 IndexNode 对象，而响应合成器则根据检索到的节点和用户查询生成自然语言响应。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f888d7b96f32557b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T08:33:23.676731Z",
     "start_time": "2024-12-13T08:33:11.299825Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f888d7b96f32557b",
    "outputId": "06883e09-1b91-4eb8-9edc-6186097d9ed7"
   },
   "outputs": [],
   "source": [
    "query_engine = index.as_query_engine(similarity_top_k=5)\n",
    "response = query_engine.query(\"介绍一下杭州的历史\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472b7736ffc5dfe2",
   "metadata": {
    "id": "472b7736ffc5dfe2"
   },
   "source": [
    "# 11、LlamaIndex 中的 NodePostProcessor(Node后处理器) 是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81cb2f00219cb5d8",
   "metadata": {
    "id": "81cb2f00219cb5d8"
   },
   "source": [
    "## NodePostProcessor定义\n",
    "在 LlamaIndex 的上下文中，NodePostProcessor 是一个在数据节点（Node）被处理之后进行额外操作的组件。这些操作可以包括但不限于数据的清洗、增强、过滤或转换等，目的是确保最终提供给LLM的数据是最优的，从而提高模型响应的质量。NodePostProcessor 是通过继承自BaseNodePostprocessor接口实现的，该接口定义了_postprocess_nodes 方法，此方法接受一系列节点并返回经过处理后的节点列表。\n",
    "在 LlamaIndex 中，NodePostProcessor 的主要作用是：\n",
    "\n",
    "1. **数据清洗和隐私保护**：NodePostProcessor 可用于对节点数据进行清洗，如移除无关的信息或者进行数据格式标准化。此外，它还能用于隐私信息的掩蔽（masking）。例如，NERPIINodePostprocessor 就是用来识别并掩蔽个人身份信息（PII）的 NodePostProcessor。通过使用命名实体识别（NER）模型，它可以检测并替换掉节点中的 PII 。\n",
    "2. **数据增强**：另一个重要的功能是数据增强，这可以通过添加前后文相关的其他节点来实现，以此来丰富LLM的上下文理解。例如，PrevNextNodePostprocessor 可以用来向前或向后扩展节点，以便为模型提供更多的上下文信息，这对于提高问答系统的准确性和连贯性尤其有用 。\n",
    "3. **结果重排**：在一些场景下，NodePostProcessor 也可以用来对检索到的结果进行重排。通过重新评估和排序检索出的文档片段，可以确保最相关的信息优先呈现给用户。这种重排可以通过自定义 Rerank 功能的 NodePostProcessor 来实现，例如在 RAG（Retrieval-Augmented Generation）系统中 。\n",
    "4. **动态捕捉视频后处理**：虽然这不是 LlamaIndex 的直接应用场景，但从更广泛的视角来看，NodePostProcessor 的概念也被应用到了诸如动态捕捉视频数据的后处理中。在这种情况下，它被用来处理节点数据以创建运动捕捉视频 。尽管这超出了 LlamaIndex 的原始设计范围，但它展示了 NodePostProcessor 概念的灵活性和可扩展性。\n",
    "\n",
    "## 相似性节点后处理器 SimilarityPostprocessor\n",
    "用于删除低于相似性分数阈值的节点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "yTEuDfzZK5GM",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yTEuDfzZK5GM",
    "outputId": "1efc6a20-ec7f-4aea-c74b-e1bcc645b810"
   },
   "outputs": [],
   "source": [
    "retriever = index.as_retriever(similarity_top_k=3)\n",
    "nodes = retriever.retrieve(\"第50题是sql答案是什么？\")\n",
    "print(len(nodes))\n",
    "print(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d64db01b958f24a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7d64db01b958f24a",
    "outputId": "468fef72-a5f4-4c68-c95b-032ede7d9eb0"
   },
   "outputs": [],
   "source": [
    "\n",
    "from llama_index.core.postprocessor import SimilarityPostprocessor\n",
    "postprocessor = SimilarityPostprocessor(similarity_cutoff=0.5)\n",
    "nodes = postprocessor.postprocess_nodes(nodes)\n",
    "print(len(nodes))\n",
    "print(nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "659938c723712349",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T09:15:29.628651Z",
     "start_time": "2024-12-13T09:15:22.222168Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "659938c723712349",
    "outputId": "1f6378ab-257a-4a39-9f57-bb456a6cdc14"
   },
   "outputs": [],
   "source": [
    "# configure retriever\n",
    "retriever = VectorIndexRetriever(\n",
    "    index=index,\n",
    "    similarity_top_k=10,\n",
    ")\n",
    "\n",
    "# # configure response synthesizer\n",
    "# response_synthesizer = get_response_synthesizer()\n",
    "\n",
    "# assemble query engine\n",
    "query_engine = RetrieverQueryEngine(\n",
    "    retriever=retriever,\n",
    "    # response_synthesizer=response_synthesizer,\n",
    "    node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=0.4)],\n",
    ")\n",
    "\n",
    "# query\n",
    "response = query_engine.query(\"第50题是sql答案是什么？\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda1a4cf47290f64",
   "metadata": {
    "id": "bda1a4cf47290f64"
   },
   "source": [
    "## 其它后处理器 Node Postprocessor\n",
    "https://docs.llamaindex.ai/en/stable/module_guides/querying/node_postprocessors/node_postprocessors/#metadatareplacementpostprocessor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d251844f1265dc3b",
   "metadata": {
    "id": "d251844f1265dc3b"
   },
   "source": [
    "# 12、嵌入及查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f19b7e78b4d81006",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T10:35:57.762887Z",
     "start_time": "2024-12-13T10:35:57.758608Z"
    },
    "id": "f19b7e78b4d81006"
   },
   "outputs": [],
   "source": [
    "# 索引存储路径\n",
    "INDEX_PATH = \"weicheng-index\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c778efb75f2f6a09",
   "metadata": {
    "id": "c778efb75f2f6a09"
   },
   "source": [
    "## 加载文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f4b4a616dff1db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T10:32:53.007179Z",
     "start_time": "2024-12-13T10:32:52.999939Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "52f4b4a616dff1db",
    "outputId": "706dce38-1981-414a-e04f-6ae6dd3ad510"
   },
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(input_files=['./data/围城.txt']).load_data()\n",
    "print(len(documents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff07574cdcef6ec3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T10:32:14.648908Z",
     "start_time": "2024-12-13T10:32:14.643321Z"
    },
    "id": "ff07574cdcef6ec3",
    "outputId": "e936bd27-8cc2-4bfd-acd0-0e2567dfef37"
   },
   "outputs": [],
   "source": [
    "# 文档前500字\n",
    "documents[0].text[:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cfde29e4d500c6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T10:33:22.523095Z",
     "start_time": "2024-12-13T10:33:22.518312Z"
    },
    "id": "2cfde29e4d500c6c",
    "outputId": "96594bc8-d6d9-47d5-fb70-da7fa8ebdfba"
   },
   "outputs": [],
   "source": [
    "# 文档长度\n",
    "len(documents[0].text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf0c4dfe9761c9b",
   "metadata": {
    "id": "1bf0c4dfe9761c9b"
   },
   "source": [
    "## 创建向量索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d615cbd1f27d2f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T10:40:15.039548Z",
     "start_time": "2024-12-13T10:40:14.736687Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4d615cbd1f27d2f3",
    "outputId": "5bf25669-4593-49ac-9106-a4a581304bff"
   },
   "outputs": [],
   "source": [
    "# 切分node\n",
    "node_parser = SentenceSplitter.from_defaults()\n",
    "nodes = node_parser.get_nodes_from_documents(documents)\n",
    "print(len(nodes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5ec6b5970f76eb7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T11:11:02.893776Z",
     "start_time": "2024-12-13T10:42:03.297668Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "58751f482dc94507b341789493d08eaa",
      "84b1b13614754733bcd3e901ccab9cf7",
      "5a59cf22a4704ae2b22263cac26ba3ff",
      "a5bda2490a2043e59b686964390eda89",
      "dfcc947809614556a6c9c9091bb7eba1",
      "e678c5527db744e487f9122b4180fd92",
      "1195d088efec4b8e942dedc5b5f55956",
      "11fb6644cf864e0fb6cc4f79a111341e",
      "ef5d98a0b7b148a8886318f88c688e3d",
      "22b9742e584b4e50a5a0ddb09d71b78b",
      "5c4442cbc7c04657b26adea560f8e12a"
     ]
    },
    "id": "f5ec6b5970f76eb7",
    "outputId": "78f9ab81-b8c6-4e9d-c0de-210595b2a11e"
   },
   "outputs": [],
   "source": [
    "# 创建并持久化索引\n",
    "import os\n",
    "\n",
    "if not os.path.exists(INDEX_PATH):\n",
    "    # 基于Node创建向量索引\n",
    "    index = VectorStoreIndex(nodes, show_progress=True)\n",
    "    index.storage_context.persist(persist_dir=INDEX_PATH)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a2d68fc29672f6",
   "metadata": {
    "id": "57a2d68fc29672f6"
   },
   "source": [
    "## 加载向量索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8dd7e17db2ce820",
   "metadata": {
    "id": "e8dd7e17db2ce820"
   },
   "outputs": [],
   "source": [
    "storage_context = StorageContext.from_defaults(persist_dir=INDEX_PATH)\n",
    "index=load_index_from_storage(storage_context)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dadd6da6f630292",
   "metadata": {
    "id": "dadd6da6f630292"
   },
   "source": [
    "## 基于检索器检索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93cc883f0ad83fa2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "93cc883f0ad83fa2",
    "outputId": "bef9a6f8-b1a7-4fd6-eb17-238bb7300f39"
   },
   "outputs": [],
   "source": [
    "retriever = index.as_retriever()\n",
    "nodes = retriever.retrieve(\"方鸿渐的妻子是谁\")\n",
    "# 默认 top_k=2\n",
    "print(len(nodes))\n",
    "print(\"\\n\\n\".join(node.text for node in nodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2413ac642a9d47",
   "metadata": {
    "id": "fa2413ac642a9d47"
   },
   "source": [
    "## 基于查询引擎检索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1JRF_6scTBzj",
   "metadata": {
    "id": "1JRF_6scTBzj"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.postprocessor import SentenceTransformerRerank\n",
    "\n",
    "rerank = SentenceTransformerRerank(\n",
    "    model=\"BAAI/bge-reranker-v2-m3\", top_n=5,\n",
    "    device=\"cpu\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2ea67cb25e56505",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e2ea67cb25e56505",
    "outputId": "d5f0b894-ea52-4af9-b60d-e7e16fa3b4fe"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.response_synthesizers import ResponseMode\n",
    "retriever = index.as_retriever(similarity_top_k=5)\n",
    "# 创建响应合成器\n",
    "response_synthesizer = get_response_synthesizer(\n",
    "    response_mode=ResponseMode.TREE_SUMMARIZE,\n",
    ")\n",
    "\n",
    "# create query engine\n",
    "query_engine = RetrieverQueryEngine(\n",
    "    retriever=retriever,\n",
    "    response_synthesizer=response_synthesizer,\n",
    "    node_postprocessors=[rerank]\n",
    ")\n",
    "\n",
    "# query\n",
    "response = query_engine.query(\"方鸿渐的妻子是谁\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30183b908558ffb1",
   "metadata": {
    "id": "30183b908558ffb1"
   },
   "source": [
    "## 增加top_k能否提高准确率？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce81ca4cbb005d9f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ce81ca4cbb005d9f",
    "outputId": "6534be7e-2523-4e2d-b794-88541014dec5"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.response_synthesizers import ResponseMode\n",
    "# 分别设置top_k=5, 20, 100, 1000\n",
    "retriever = index.as_retriever(similarity_top_k=2)\n",
    "# 创建响应合成器\n",
    "response_synthesizer = get_response_synthesizer(\n",
    "    response_mode=ResponseMode.TREE_SUMMARIZE,\n",
    ")\n",
    "\n",
    "# create query engine\n",
    "query_engine = RetrieverQueryEngine(\n",
    "    retriever=retriever,\n",
    "    response_synthesizer=response_synthesizer,\n",
    "\n",
    ")\n",
    "\n",
    "# query\n",
    "response = query_engine.query(\"方鸿渐在三闾大学的经历是怎样的？\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f984aa77296857",
   "metadata": {
    "id": "2f984aa77296857"
   },
   "source": [
    "# 13、重排优化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd30a6bd5fcf8be",
   "metadata": {
    "id": "cfd30a6bd5fcf8be"
   },
   "source": [
    "## 回忆与上下文窗口"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23fb249e963bf45",
   "metadata": {
    "id": "f23fb249e963bf45"
   },
   "source": [
    "使用 RAG，我们可以对许多文本文档执行语义搜索— 这些文档可能有数万个，甚至数百亿个。\n",
    "为了确保大规模搜索时间短，我们通常使用向量搜索 - 也就是说，我们将文本转换为向量，将它们全部放入向量空间，然后使用相似度度量（如余弦相似度）比较它们与查询向量的接近度。\n",
    "要使向量搜索发挥作用，我们需要向量。这些向量本质上是将一些文本背后的“含义”压缩为（通常）768 或 1536 维向量。由于我们将这些信息压缩为单个向量，因此会有一些信息丢失。\n",
    "由于这种信息丢失，我们经常看到前三个（例如）向量搜索文档会丢失相关信息。不幸的是，检索可能会返回低于我们的top_k截止值的相关信息。\n",
    "如果较低位置的相关信息可以帮助我们的 LLM 制定更好的响应，我们该怎么办？最简单的方法是增加我们返回的文档数量（增加top_k）并将它们全部传递给 LLM。\n",
    "我们在这里要衡量的指标是召回率— 即“我们检索了多少相关文档”。召回率不考虑检索到的文档总数 — 因此我们可以破解该指标，通过返回*所有内容来获得完美的召回率\n",
    "\n",
    "不幸的是，我们无法返回所有内容。LLM 对我们可以传递给它们的文本量有限制——我们称此限制为上下文窗口。一些 LLM 具有巨大的上下文窗口，例如 Anthropic 的 Claude，其上下文窗口有 100K 个标记。这样，我们可以容纳数十页的文本——那么我们是否可以返回许多文档（不是全部）并“填充”上下文窗口以提高召回率？\n",
    "再次强调，不行。我们不能使用上下文填充，因为这会降低 LLM 的召回性能——请注意，这是 LLM 召回，与我们迄今为止讨论的检索召回不同。\n",
    "\n",
    "LLM 回忆能力是指 LLM 从其上下文窗口内的文本中查找信息的能力。研究表明，随着我们在上下文窗口中放入更多标记，LLM 回忆能力会下降。当我们填充上下文窗口时，LLM 也不太可能遵循指令 — 因此上下文填充不是一个好主意。\n",
    "我们可以增加向量数据库返回的文档数量以提高检索召回率，但如果不损害 LLM 召回率，我们就无法将这些文档传递给我们的 LLM。\n",
    "\n",
    "解决此问题的方法是通过检索大量文档来最大化检索召回率，然后通过最小化进入 LLM 的文档数量来最大化 LLM 召回率。为此，我们对检索到的文档进行重新排序，只保留与我们的 LLM 最相关的文档 — 为此，我们使用重新排序。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ccef34f1903d73f",
   "metadata": {
    "id": "6ccef34f1903d73f"
   },
   "source": [
    "## 强大的重排器"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710ebe2f7de41014",
   "metadata": {
    "id": "710ebe2f7de41014"
   },
   "source": [
    "Rerank 模型（也称为交叉编码器）是一种模型，给定查询和文档对，它将输出相似度分数。我们使用此分数根据与查询的相关性对文档进行重新排序。\n",
    "在两阶段检索系统中使用重新排序器。在这些两阶段系统中，第一阶段模型（嵌入模型/检索器）从较大的数据集中检索一组相关文档。然后，使用第二阶段模型（重新排序器）对第一阶段模型检索到的文档进行重新排序。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3afc10b20f0f8b4f",
   "metadata": {
    "id": "3afc10b20f0f8b4f"
   },
   "source": [
    "## Reranker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84d63e79b6c16601",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-13T12:46:46.850266Z",
     "start_time": "2024-12-13T12:46:46.765473Z"
    },
    "id": "84d63e79b6c16601"
   },
   "outputs": [],
   "source": [
    "from llama_index.core.postprocessor import SentenceTransformerRerank\n",
    "\n",
    "rerank = SentenceTransformerRerank(\n",
    "    model=\"BAAI/bge-reranker-v2-m3\", top_n=1,\n",
    "    device=\"cpu\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Rnybpa4SJhwM",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rnybpa4SJhwM",
    "outputId": "2910f257-c3c8-4293-fbe6-acda50c9cdce"
   },
   "outputs": [],
   "source": [
    "retriever = index.as_retriever(\n",
    "    similarity_top_k=3,\n",
    ")\n",
    "\n",
    "response_synthesizer = get_response_synthesizer(\n",
    "    response_mode=ResponseMode.TREE_SUMMARIZE,\n",
    "    streaming=True,\n",
    ")\n",
    "\n",
    "query_engine = RetrieverQueryEngine(\n",
    "    retriever=retriever,\n",
    "    response_synthesizer=response_synthesizer,\n",
    "    node_postprocessors=[rerank]\n",
    ")\n",
    "\n",
    "response = query_engine.query(\"第50题的sql答案是什么？\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "UEbmW9t6RBIt",
   "metadata": {
    "id": "UEbmW9t6RBIt"
   },
   "outputs": [],
   "source": []
  }
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